Separating intended and non-intended browsing traffic in browsing history

ABSTRACT

Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

TECHNICAL FIELD

This disclosure relates generally to the field of network communicationand, more specifically, to mining user internet browsing history datafor various purposes in wireless communication systems for advancednetworks (e.g., 4G, 5G, and beyond).

BACKGROUND

Internet usage, especially through mobile devices, has beensignificantly increasing. A vast amount of data related to the internetusage is available and can be utilized to learn information about theusers that are accessing the Internet. Therefore, unique challengesexist to provide levels of service and relevant information associatedwith the data related to the internet usage.

BRIEF DESCRIPTION OF THE DRAWINGS

Various non-limiting embodiments are further described with reference tothe accompanying drawings in which:

FIG. 1 illustrates an example, non-limiting, system in accordance withone or more embodiments described herein;

FIG. 2 illustrates an example, non-limiting, method for separatingintended and non-intended browsing traffic in accordance with one ormore embodiments described herein;

FIG. 3 illustrates an example, non-limiting, method for applying samplednetwork traffic information to unsampled network traffic information toidentify intended and non-intended browsing traffic in accordance withone or more embodiments described herein;

FIG. 4 illustrates an example, non-limiting, method for utilizing adefined value to separate network traffic into intentional networktraffic and unintentional network traffic in accordance with one or moreembodiments described herein;

FIG. 5 illustrates an example, non-limiting, method for validatingsecond-level domains identified in observed browsing history traffic inaccordance with one or more embodiments described herein;

FIG. 6 illustrates an example, non-limiting, method for exemptinginclusion of second-level domains in a blacklist in accordance with oneor more embodiments described herein;

FIG. 7 illustrates an example, non-limiting, method for utilizing asubstring match to identify an unintended domain name in accordance withone or more embodiments described herein;

FIG. 8 illustrates an example, non-limiting, method for combining adetermined blacklist with one or more external lists to create a mergedlist in accordance with one or more embodiments described herein;

FIG. 9 illustrates an example block diagram of an example mobile handsetoperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein;and

FIG. 10 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein.

DETAILED DESCRIPTION

One or more embodiments are now described more fully hereinafter withreference to the accompanying drawings in which example embodiments areshown. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various embodiments. However, the variousembodiments can be practiced without these specific details (and withoutapplying to any particular networked environment or standard).

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate separatinguser-intended and non-user intended browsing traffic in user browsinghistory data. In one embodiment, described herein is a system that cancomprise a processor and a memory that stores executable instructionsthat, when executed by the processor, facilitate performance ofoperations. The operations can comprise determining respectivecontradiction values for second-level domains of a group of second-leveldomains in observed browsing history traffic. The operations can alsocomprise separating intended network traffic from non-intended networktraffic based on the respective contradiction values. The respectivecontradiction values can indicate levels of inconsistency between theobserved browsing history traffic and a determined popularity ranking.

According to an implementation, separating the intended network trafficfrom the non-intended network traffic can comprise identifying asecond-level domain of the group of second-level domains as thenon-intended network traffic. The identification can be based on acontradiction value for the second-level domain being determined to beabove a threshold level. Further to this implementation, the operationscan comprise adding the second-level domain to a list of identifiednon-intended domains and utilizing the list to facilitate the separatingthe intended network traffic from the non-intended network traffic.

Determining the respective contradiction values can comprise, accordingto an implementation, evaluating respective popularities of thesecond-level domains and associated subdomains in the observed browsinghistory data and respective third-party popularity rankings of thesecond-level domains.

In accordance with some implementations, the operations can compriseinitiating a network connection with a second level domain selected fromthe group of second-level domains. Further, the operations can comprisedetermining the second level domain is a valid second level domain basedon a successful network connection with the second level domain.

According to some implementations, the operations can compriseinitiating a network connection with a second level domain selected fromthe group of second-level domains. The operations can also comprisedetermining the second level domain is an invalid second level domainbased on an unsuccessful network connection with the second leveldomain. Further, the operations can comprise adding the second leveldomain to a list of identified non-intended domains. The list can beutilized to facilitate the separating the intended network traffic fromthe non-intended network traffic.

The operations can comprise, according to some implementations,identifying a second level domain as an intended domain. Further, theoperations can comprise adding the second level domain to a list ofsecond level domains that are exempted from being identified asnon-intended domains.

In some implementations, the operations can comprise generating a listof substrings and identifying a second level domain as a non-intendeddomain based on a determination that the second level domain comprises asubstring included in the list of substrings.

According to some implementations, the operations can comprise includinga second-level domain of the group of second-level domains to a list ofidentified non-intended domains. Further, the operations can comprisemerging a third party created list of non-intended domains to the listof identified non-intended domains.

The observed browsing history traffic can be a sample of availablebrowsing history traffic. In these implementations, the operations cancomprise including the second-level domains associated with thenon-intended network traffic in a first data store and the second-leveldomains associated with the intended network traffic in a second datastore. Further, the operations can comprise applying the first datastore and the second data store to other available browsing historytraffic not including the sample of available browsing history traffic.In addition, the operations can comprise separating the other availablebrowsing history traffic into the intended network traffic and thenon-intended network traffic.

In an example, the non-intended network traffic can comprise third-partyrequests. In an additional or alternative example, the non-intendednetwork traffic can comprise passive system requests.

Another embodiment relates to a method that can comprise evaluating, bya system comprising a processor, respective contradiction scores forsecond-level domains of a group of second-level domains of observedbrowsing history traffic. The method can also comprise identifying, bythe system, intended network traffic and non-intended network traffic ofthe observed browsing history traffic based on the respectivecontradiction scores. The respective contradiction scores can indicate alevel of inconsistency between the observed browsing history traffic andan external popularity ranking.

The observed browsing history traffic can be first observed browsinghistory traffic and the method can comprise generating, by the system, afirst list that comprises second level domains associated with theintended network traffic and a second list that comprises the secondlevel domains associated with the non-intended network traffic. Themethod can also comprise separating, by the system, second level domainsof second observed browsing history traffic based on an application ofthe first list and the second list to the second observed browsinghistory traffic.

In some implementations, the method can comprise identifying, by thesystem, a first second-level domain of the group of second-level domainsas intended network traffic based on a first contradiction score of therespective contradiction scores not satisfying a threshold value.Further, the method can comprise identifying, by the system, a secondsecond-level domain of the group of second-level domains as thenon-intended network traffic based on a second contradiction score ofthe respective contradiction scores satisfying the threshold value.

According to some implementations, the evaluating the respectivecontradiction scores can comprise evaluating respective popularities ofthe second-level domains and associated subdomains in the observedbrowsing history data and respective external popularity rankings of thesecond-level domains.

The method can comprise, according to some implementations, generating,by the system, a list of substrings and identifying, by the system, asecond level domain as a non-intended domain based on a determinationthat the second level domain comprises a substring included in the listof substrings.

In some implementations, the method can comprise including, by thesystem, a second-level domain of the group of second-level domains in alist of identified non-intended domains. Further, the method cancomprise merging, by the system, an external list of non-intendeddomains to the list of identified non-intended domains.

According to another example, the method can comprise initiating anetwork connection with a second level domain selected from the group ofsecond-level domains. The method can also comprise determining thesecond level domain is a valid second level domain based on a successfulnetwork connection with the second level domain or is an invalid secondlevel domain based on an unsuccessful network connection with the secondlevel domain. Further, the method can comprise adding the invalid secondlevel domain to a list of identified non-intended domains, wherein thelist is utilized to facilitate the separating the intended networktraffic from the non-intended network traffic.

In accordance with another example, the method can comprise initiating anetwork connection with a second level domain selected from the group ofsecond-level domains. Further, the method can comprise determining thesecond level domain is an invalid second level domain based on anunsuccessful network connection with the second level domain. Inaddition, the method can comprise adding the second level domain to alist of identified non-intended domains, wherein the list is utilized tofacilitate the separating the intended network traffic from thenon-intended network traffic.

In yet another example, the method can comprise identifying a secondlevel domain as an intended domain. The method can also comprise addingthe second level domain to a list of second level domains that areexempted from being identified as non-intended domains.

A further, embodiment relates to a machine-readable storage medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations. The operations can comprisedetermining, for a first device selected from a group of devices in awireless network, first domains of first browsing history traffic. Theoperations can also comprise identifying the first domains as a firstgroup of domains and a second group of domains. The first group ofdomains can comprise domains associated with intentional networktraffic. The second group of domains can comprise domains associatedwith unintentional network traffic. Further, the operations can compriseapplying the first group of domains and the second group of domains tosecond browsing history traffic associated with a second device selectedfrom the group of devices.

In some implementations, the operations of the machine-readable storagemedium can comprise generating a whitelist that comprises the firstgroup of domains and a blacklist that comprises the second group ofdomains. Further, the operations can comprise applying the whitelist andthe blacklist to devices in the group of devices, including the seconddevice.

Increasing internet usage (especially usage through mobile devices)presents the opportunity to mine user browsing history data for variouscommercial products. To effectively identify user behaviors andinterests, browsing traffic that users actively intend to request shouldbe identified. The various aspects discussed herein can filter outnon-user-intended traffic, such as third-party requests and/or passivesystem requests (e.g., ads, geo-positioning), which are present in largeproportions (e.g., around 80% to about 90% or more) in browsing historyof various devices (e.g., mobile devices, desktop devices, laptopdevices, and other User Equipment (UE)). By identifying and filteringout the non-user-intended traffic, a much smaller but more informativeset of browsing activities can be revealed for better understanding ofusers. Without the filtering, insights gathered from naive post-hocanalysis of raw browsing data may not be accurate.

The various aspects discussed herein can greatly increase the usabilityand value of a set of browsing data and can enhance various applicationsor products that derive intelligence from such data. Examples ofapplications and/or products include, but are not limited to, usersegmentation and profiling, targeted advertising, content and/or productrecommendation, and content storage and delivery optimization. Entitiesthat can benefit from the disclosed aspects include, but are not limitedto, entities that own or collect browsing data and are interested indeveloping data products that have higher values than the raw browsingdata; telecommunication entities interested in optimizing contentdelivery through networks; entities that own and deliver content and areinterested in optimizing content recommendation, storage, and delivery;and entities that are interested in targeted advertising orrecommendation of content, product, event, and so on; such advertisingor recommendation can also be implemented cross-platform.

As internet usage and penetration (especially usage through mobiledevices) continues to increase rapidly in the next few years (especiallyfast in some countries), more entities will come to practice and valuebrowsing data mining for an increasingly wider array of applications andproducts. The disclosed aspects can be utilized to considerably boosttheir capabilities to do so. Traditional methods either give verypartial solutions to this filtering problem or require user-side access,which can be expensive and intrusive. The disclosed aspects can providea complete filtering solution, filtering out non-user-intended trafficin proportions consistent with external and internal experiments andstudies. Further, the disclosed aspects do not need user-side access oraction. The disclosed aspects also do not need manual annotation,labeling, or extensive experimentation. The disclosed aspects can berobust to coarse aggregation or recording of times and domains in thebrowsing data and can be easily adapted to desktop/laptop browsingtraffic or browsing traffic from other countries. Further, the disclosedaspects can provide output filtering lists that can be easily computed,updated, interpreted, and customized for different use cases.

The number of internet users worldwide has increased 6.1% in 2017compared to 2016, and nearly four in five use a mobile phone to accessthe internet. In the United States, internet usage through mobiledevices has overtaken the amount of internet usage throughdesktops/laptops in 2014, both in number of users and time spent usingthe internet. This has led to great potential and challenges intargeting advertising, content recommendation, content storage, anddelivery optimization. Mining user browsing history data provides anessential pathway for such applications.

However, to effectively identify user behaviors and interests, browsingtraffic that users actively intend to request should be identified. Suchuser-intended traffic is only present in small proportions (around 10%to about 20%) in both mobile and desktop/laptop browsing history data.The remaining majority of the history consists of a large amount ofcollateral third-party requests and/or passive system requests (furtherdetails will be provided below with respect to explanations ofnon-user-intended traffic), which are not nearly as insightful aboutuser behaviors and interests.

The various aspects can separate user-intended and non-user-intendedtraffic from user browsing history data. This can be useful for bettermodeling of user behaviors and interests, and better results inapplications such as targeted advertising and content delivery. Thevarious aspects were developed using mobile browsing history data fromthe United States, although the same aspects can be easily extended todesktop/laptop browsing data and/or browsing data from other countries.It is that this separation is different in nature from the separation ofhuman and non-human browsing traffic. Non-human traffic, for example,can be automated visitation traces generated by bots and crawlers thatare not controlled by individual users. This is also different in naturefrom identifying domains that are malicious or pose some securitythreats. The various aspects look within all browsing traces generatedby personal devices, regardless of whether malicious, and filteringbased on whether requests are actively intended by users and helpful foruncovering user interests.

Non-user intended traffic includes third-party requests and/or passivesystem requests. Third-party requests can occur when a user makes anHTTP (hypertext transfer protocol) request to load a webpage (e.g.example_domain.com), it receives an HTML file from the server associatedwith its domain. Direct transactions with this intended server owned byexample_domain can be referred to as “first-party” requests. However,the webpage can include additional elements, such as pictures, videos,or Javascript code, from parties external to the intended server ofexample_domain, and loading of such additional elements are“third-party” requests. External companies can pay or offer freeservices to example_domain to have their elements displayed on its page.These third-party elements can be advertisements, traffic trackers,social media tools, etc.

It has been estimated that about 90% of the top million domains initiaterequests to on average 9.5 distinct third-parties on desktop/laptop,mostly through invisible elements on the webpages. This suggests theproportion of third-party requests out of all requests is very high atroughly around 80-90%. This quantity also aligns with various testingand experiments for mobile browsing. Such a large volume of third-partyrequests considerably masks and dilutes the users' true browsingintentions.

Passive system requests can occur when a device's operating system orvarious applications may initiate a large volume of maintenance andutility requests without active or explicit interactions with the user,for location positioning, periodically checking, uploading, ordownloading software or content, and so on, for example. Most of thistype of traffic does not contain much information about the users'active browsing interests or behaviors.

Referring initially to FIG. 1, illustrated is an example, non-limiting,system 100 in accordance with one or more embodiments described herein.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity. To create a blacklist102, samples with Second Level Domains (SLDs) can be generated andcontradiction scores can be determined. Optionally, the SLDs can bevalidated, SLDs can be exempted, bad substrings can be determined,and/or merged with external lists.

In further detail, to generate samples with SLDs, browsing history data104 from one or more mobile devices can be received. For example, thebrowsing history data can be received as production data feed to acentral database or to multiple databases, which can be distributeddatabases. The feed can be processed to ensure user privacy, withencryption of MSISDNs and coarse aggregation of timestamps (e.g., every5 minutes) and domains (e.g., shortened Uniform Resource Locators (URLs)to second-level domains, or one- or two-level lower only).

The various aspects can be implemented on a sample of this data (e.g.,sample data 106) that is sufficiently large to be representative oftraffic of all devices. For example, the sample data 106 can represent,at a minimum, one month of usage for 1% of all devices. The shortenedURLs in this sample can be converted 108 to its corresponding SLDs. Oncea blacklist 102 is generated from this sample, it can be applied tofilter 110 the entire feed.

A determination can be made whether visits to an SLD is likely to beactively intended by users or is likely to be activity not intended byusers. For example, for each SLD, a score can be computed based on atleast two factors, namely, empirical prevalence 112 and third-partypopularity ranking 114. The empirical prevalence p relates to howpopular a particular SLD and its subdomains are based on the browsingdata. The third-party popularity ranking r relates to how a particularSLD ranks in terms of popularity according to one or more third-partysources (e.g. Quantcast, Alexa). Further details related to these twofactors and how the score is determined based on these factors will beprovided below.

As it relates to empirical prevalence p, for each SLD, this is thepercentage of devices that have at least one request for this domain orany of its subdomains. This captures the “reach” of this SLD in thedevice pool. This quantity is usually very large for advertisementaggregators, CDNs, traffic generated by operating systems, and verypopular sites. This metric has a very long tail and may follow the powerlaw.

As it relates to third-party popularity ranking r, some third-partiespublish rankings of SLDs by popularity. These rankings primarily aim torank user-intended traffic. For popular domains that users visit often,the ranking numerical quantities are typically low (e.g. ranking 1 or2). For domains of ad aggregators, CDNs, or OS providers, the rankingnumerical quantities are typically high (e.g. ranking 97638), or theydon't appear on the ranking list at all.

The following are additional notes related to third-party popularityranking r. A first third-party ranker 116 can publish the top around90,000 domains by popularity, and a second third-party ranker 118 canpublish the top million. Both lists can be downloaded for free. Bothlists are updated fairly often, but typically there is no substantialdifference from day to day and the lists can be updated at longerintervals (e.g., about every 3 months). Both rankings are fordesktop/laptop usage. Mobile usage can have a slightly differentpattern. A third-party ranker might also provide a much shorter listspecific to mobile usage. Further, one third-party ranker can provide alist that is more geared towards the United States population (althoughit also provides shorter lists for some other countries), while a listprovided by another third-party ranker is more international. In somecases, a list can include a non-trivial number of “Hidden Profiles”where the domain name is hidden (e.g., missing). Both lists, to a smalldegree, still rank some domains from ad aggregators, CDNs, OS providers,although they typically do not rank these domains as very high (notnearly as high as what was observe from experimental data). On thisfront, the lists can have different levels of quality.

At least in part due to the above, the two lists can be unioned (orcombined) when the third-party popularity ranking r is constructed. Whenmerging, a first list can be considered the primary list; domains notlisted in the first list (because of, for example, Hidden profile,having a more international audience) but are listed in a second listare joined to the first list, along with a ranking of the second list.

A contradiction score 120 can be determined. For each SLD, itscontradiction score is computed as p*r{circumflex over ( )}a, where p isits empirical prevalence, r is its third-party popularity ranking, and ais a constant between 0 and 1 set uniformly across all SLDs. This scoreis designed so that only domains heavily visited by nonuser-intendedtraffic gives high contradiction scores. It is noted that thecontradiction score 120 provided is merely an example formula and manyother functions relating to two quantities p and r are possible,provided that such functions distinguish the case where p is large and ris large. The following explains at least four scenarios.

A domain with many visits mostly intended by users has a large p and asmall r. Therefore, the contradiction score is low. A domain with fewvisits mostly intended by users has a small p and a large r and, thus,the contradiction score is low. A domain with many visits mostly notintended by users has a large p and a large r and the contradictionscore is high—this is the case that the disclosed aspects are intendedto identify. A domain with few visits mostly not intended by users has asmall p and a small r and, therefore, the contradiction score is low.This fourth case is rare because many ad/analytics/OS domains have ortry to have large reach in users and large volumes of traffic in orderto stay competitive in its market.

A cutoff of this contradiction score (e.g., a threshold level) can bedecided. Thus, all domains whose contradiction scores are higher thanthis cutoff are included in the output blacklist.

The constant a is a number between 0 and 1 that aims to dampen theimpact of third-party popularity ranking in the computation ofcontradiction score. This is motivated by the fact that r is not perfectin quality; a number of ad/analytics domains are ranked in thethird-party rankings although their rankings are not very high. In theimplemented version, a is taken to be 0.5 for all SLDs. Changing valuesof a does not substantially change the output blacklist, although thecutoff of contradiction score for what to include in the blacklist mayneed to be adjusted.

If a domain is not on the third-party ranking list, the domain could beassigned a nominal ranking (e.g. 1,000,001). Mathematicallyequivalently, a cutoff could be imposed on its p value alone to decidewhether this domain would be included in the blacklist.

The cutoff can be determined by inspection. The cutoff point can be setroughly to where there starts to be more domains with mostlyuser-intended traffic than those with mostly non-user-intended traffic.In an implementation, the default value of 10.1 can be utilized, as anexample. If the number of users and date range for the sample or thevalue of a is changed, this cutoff may need to be adjusted.

This cutoff may need to be tuned a very small number of times a year bylooking at the tail of the blacklist generated. A machine learningclassifier can be built to automate this decision, using informationsuch as past blacklist memberships, memberships in external lists,and/or the fact that the disclosed aspects are aiming to filter around80 to 90 percent of the traffic.

The cleaning process might not be as sensitive to this cutoff. The headof the blacklist (e.g., domains with the highest contradiction scores)are the main culprits in generating the largest amounts ofnon-user-intended traffic, and domains near the tail of the blacklisttypically do not get nearly as much volume and reach.

There are at least four optional implementations, indicated at 122, andany subset, or all, of the at least four optional implementations can beadded to the blacklist construction route. These optionalimplementations include validate SLDs, exempt SLDs, detect badsubstrings, and/or combine with external lists. Further details relatedto these optional implementations will be discussed below with respectto FIGS. 5-8.

The blacklist can be updated continuously, continually, periodically, atvariable intervals, or based on a parameter (e.g., at least a set of thedata has changed, new data has been entered, a query has been executedon the data, and so forth). In one specific, non-limiting example, theblacklist can be updated every three months. To filter traffic in thebrowsing history data based on a blacklist, each shortened URL obtained,u, can be matched with each SLD on the blacklist, b, by “lower leveldomain match” (e.g., either an exact match, or b with a dot prepended isa substring of u).

There can also be standalone code for validating SLD and convertingshortened (or any) URL to its SLD. There can also be a curated list ofbad substrings to detect and a list of domains to exempt, which can bespecific to some use cases.

Methods that can be implemented in accordance with the disclosed subjectmatter, will be better appreciated with reference to the following flowcharts. While, for purposes of simplicity of explanation, the methodsare shown and described as a series of blocks, it is to be understoodand appreciated that the disclosed aspects are not limited by the numberor order of blocks, as some blocks can occur in different orders and/orat substantially the same time with other blocks from what is depictedand described herein. Moreover, not all illustrated blocks can berequired to implement the disclosed methods. It is to be appreciatedthat the functionality associated with the blocks can be implemented bysoftware, hardware, a combination thereof, or any other suitable means(e.g. device, system, process, component, and so forth). Additionally,it should be further appreciated that the disclosed methods are capableof being stored on an article of manufacture to facilitate transportingand transferring such methods to various devices. Those skilled in theart will understand and appreciate that the methods could alternativelybe represented as a series of interrelated states or events, such as ina state diagram.

Various benefits and advantages can be realized with the disclosedaspects. For example, provide herein is an effective way to separate,from existing user browsing history data, browsing activities that areactively intended by users and those that are not. Some methods eithergive very partial solutions, which do not effectively solve the problem,or require user-side access, which may be expensive and intrusive. Thedisclosed aspects provide a complete filtering solution, filtering outnon-user-intended traffic in proportions consistent with external andinternal experiments and studies; does not require user-side access oraction; and does not require manual annotation, labeling, or extensiveexperimentation. Further, the disclosed aspects are robust to coarseaggregation or recording in times and domains in the browsing data; canbe easily adapted to desktop/laptop browsing traffic, browsing trafficfrom specific populations (e.g. those from a particular country), orbrowsing traffic of various granularities and level of domains; givesoutput filtering lists that can be easily computed, updated,interpreted, and customized for different use cases.

Other benefits and advantages are that the separation of user- andnon-user-intended traffic is an essential first-step for effectivemining of the browsing history data from either mobile ordesktop/laptop. Without this, insights gathered from naive post-hocanalysis of browsing data may not be accurate, because a predominantproportion of this data is not intended by the users and may not bemeaningful.

The filtering, as discussed herein can greatly increase the usabilityand value of a set of browsing history data and can enhance variousapplications or products that make use of intelligence derived from it.Examples of such applications include, but are not limited to, usersegmentation and profiling, targeted advertising, content or productrecommendation, content storage and delivery optimization. Entities thatcan benefits from the disclosed capabilities include, but are notlimited to, entities that own or collect browsing data and areinterested in developing data products that have higher values than rawbrowsing data. Other entities include telecommunication businessesinterested in optimizing content delivery through the network;businesses that own and deliver content and are interested in optimizingcontent recommendation, storage, and delivery, and/or any business thatis interested in targeted advertising or recommendations of content,product, event, and so on. The advertising and recommendation can takeplace either on the platform where the browsing data is collected, orcross-platform (if linkage is possible), which may be especially helpfulfor profiling new clients who have not interacted extensively with thesystem of a business to create enough behavioral data.

By way of example and not limitation, the various aspects discussedherein can use mobile browsing history data to inform targetedadvertising and recommend shows on various television platforms, andforecast and optimize network delivery of large video content through anetwork. The browsing history data can be joined with data from varioustelevision platforms and locations to provide multi-facetedcross-platform user profiles. Filtering of non-user-intended browsingactivity as discussed herein can lead to better results in theseefforts.

As internet usage (especially that through mobile) increases rapidly inthe coming years, more businesses will come to practice and value themining of browsing data for an increasingly wider array of applicationsor products. The disclosed aspects can increase the intelligence contentin browsing data, which can lead to better results in theseapplications.

FIG. 2 illustrates an example, non-limiting, method 200 for separatingintended and non-intended browsing traffic in accordance with one ormore embodiments described herein. The method 200 can be implemented bya system comprising a processor.

As discussed herein, various aspects relate to producing a list ofsecond-level domains (SLD), where visits to these domains and theirsubdomains are mostly not actively intended by users. The list can beutilized to filter out all traffic to each SLD on this list and itssubdomains. The list can be referred to as a “blacklist.” It is notedthat an SLD is directly below the top-level domain, which is the lastpart of the domain name. For example, the SLD ofwww.example_domain.com/example_subdomain is example_domain.com. Inanother example, the SLD of www.example_subdomain.example_domain.co.ukis example_domain.co.uk.

At the SLD level, some domains and their subdomains can attract amixture of user-intended and non-user-intended traffic. For example,visits to some subdomains of apple.com may be user intended appleproduct browsing on its retail page, but a much larger volume of visitsto some subdomains may be generated by the iOS operating systembackground activities for iPhones, such as location tracking andsoftware updates. Without the full URL, it is challenging to tell themapart. Whether or not an SLD is on the output blacklist depends on theproportion of its traffic that is non-user-intended in the average case.

Unavoidably, some small numbers of false positives and false negativesare incurred because the full URL information and the ground truths arenot available in some implementations. However, the disclosed aspectsstill allow for effectively filtering out the bulk of ad, analytics,infrastructure, platform, content delivery networks (CDNs), andoperating system background traffic, to reveal a much smaller but a muchmore informative set of browsing traffic that corresponds to usersinterests and behaviors.

The method 200 starts, at 202, with evaluating respective contradictionscores for second-level domains of a group of second-level domains ofobserved browsing history traffic. For example, the observed browsinghistory traffic can be a subset (e.g., a sample) of the browsing historytraffic that is available.

Further, at 204, intended network traffic and non-intended networktraffic of the observed browsing history traffic can be identified basedon the respective contradiction scores. The respective contradictionscores can indicate a level of inconsistency between the observedbrowsing history traffic and an external popularity ranking. Theintended network traffic information can be retained in a first list(e.g., a whitelist) and the non-intended network traffic information canbe retained in a second list (e.g., a blacklist). The lists can beapplied to other network traffic.

FIG. 3 illustrates an example, non-limiting, method 300 for applyingsampled network traffic information to unsampled network trafficinformation to identify intended and non-intended browsing traffic inaccordance with one or more embodiments described herein. The method 300can be implemented by a system comprising a processor.

At 302 respective contradiction scores for sampled domain traffic ofobserved browsing history traffic is determined. At 304, intendednetwork traffic is separated from non-intended network traffic in theobserved browsing history traffic. Further, at 306, a first list thatcomprises second level domains associated with the intended networktraffic and a second list that comprises the second level domainsassociated with the non-intended network traffic are generated.

Second level domains of second observed browsing history traffic can beseparated, at 308. The separation can be based on an application of thefirst list and the second list to the second observed browsing historytraffic. Thus, the second observed browsing history traffic can beseparated into intended network traffic and non-intended networktraffic.

FIG. 4 illustrates an example, non-limiting, method 400 for utilizing adefined value to separate network traffic into intentional networktraffic and unintentional network traffic in accordance with one or moreembodiments described herein. The method 400 can be implemented by asystem comprising a processor.

At 402, a threshold value related to intended network traffic andunintended network traffic can be defined. The intended network trafficcan be traffic that a user intentionally initiated (e.g., a request toaccess a website associated with a news organization). The unintendednetwork traffic can be traffic that the user unintentionally initiated(e.g., advertisement websites that are accessed when the newsorganization website is accessed). According to some implementations,the unintended network traffic can be traffic dynamically accessed by anoperating system associated with a user equipment and/or one or moreapplications that are downloaded on the user equipment and/or that areexecuting on the user equipment.

According to some implementations, calculated values at or above thedefined value can indicate network traffic that is unintended networktraffic and calculated values below the defined value can indicatenetwork traffic that is intended network traffic. However, the disclosedaspects are not limited to this implementation. Instead, values at orabove the defined value can indicate network traffic that is intendednetwork traffic and calculated values below the defined value canindicate network traffic that is unintended network traffic.

At 404, contradiction scores for domains accessed during at least aportion of a browsing session can be calculated. For example, only aselected portion of a browsing history might be sampled according to thevarious aspects discussed herein, while other portions of the browsinghistory are not sampled. However, according to some examples, an entirebrowsing history is sampled. In other examples, subsets of browsinghistory associated with two or more sources are sampled.

The one or more browsing sessions, or portions thereof, that are sampledcan comprise one or more second-level domains that are accessed and/orattempted to be accessed during the sampling period. A firstsecond-level domain of the one or more second-level domains can beidentified, at 406, as intended network traffic based on a firstcontradiction score of the respective contradiction scores notsatisfying a threshold value. Further, at 408, a second second-leveldomain of the group of second-level domains can be identified as thenon-intended network traffic based on a second contradiction score ofthe respective contradiction scores satisfying the threshold value.

According to an additional or alternative implementation, the firstsecond-level domain can be added to a white list that comprises alisting of second-level domains determined to be associated withintentional network traffic. For example, the second-level domainsincluded in the whitelist can comprise domains that were specificallyrequested by a user (e.g., an Internet marketplace website, a websitefor hobbyists, a website that hosts encyclopedic knowledge, and so on).

In accordance with an additional or alternative implementation, thesecond second-level domain can be added to a blacklist that comprises alisting of second-level domains determined to be associated withunintentional network traffic. For example, the second-level domainsincluded in the blacklist can be domains associated with advertisingwebsites, adware, or other websites not intentionally accessed by theuser.

FIG. 5 illustrates an example, non-limiting, method 500 for validatingsecond-level domains identified in observed browsing history traffic inaccordance with one or more embodiments described herein. The method 500can be implemented by a system comprising a processor.

For the optional implementation that comprises validation of the SLDs,it can be determined whether a connection with an SLD/IP address can beinitiated. If not, the SLD can be deemed invalid and included in theoutput blacklist. The blacklist can have an additional column for abinary flag of whether this SLD/IP address was found to be invalid.

The method 500 starts, at 502, and a network connection can be initiatedwith a second level domain selected from a group of second-leveldomains. At 504, a determination can be made whether the initiatednetwork connection successfully accessed the targeted domain. If thetargeted domain was successfully accessed (“YES), at 506, the secondlevel domain can be identified as a valid domain. According to someimplementations, at 508, the second level domain can be included in adata store that comprises intended network traffic (e.g., a whitelist).

If the determination at 504 is that the targeted domain was notsuccessfully accessed (“NO”), at 510, the second level domain can beidentified as an invalid domain. According to some implementations, at512, the second level domain can be included in a data store thatcomprises unintended network traffic (e.g., a blacklist).

For example, a list of all unique SLDs/IP addresses can be constructedfrom the data. Then, it can be determined whether each IP address/SLD isin the cached dictionary of SLD validity. If yes, its correspondingvalidity status can be read out. If not, do below: If it follows thepattern of an IP address, a determination can be made whether it is avalid IP address. If yes, then it is valid. If no, then it is not valid.

If it follows the pattern of an SLD, a determination can be made whetheran http or https connection can be initiated with this SLD (with orwithout prepending “www.” to the SLD). If yes, then it is deemed valid.If no, try a defined number of more times (e.g., nine more times) toinitiate the connection. Stop and categorize as valid as soon as one ofthe attempts is successful; categorize as invalid if it fails all times(e.g., all 10 times in the above example). Cache this {IP address/SLD:valid/invalid} pair into a dictionary (e.g., a data store).

FIG. 6 illustrates an example, non-limiting, method 600 for exemptinginclusion of second-level domains in a blacklist in accordance with oneor more embodiments described herein. The method 600 can be implementedby a system comprising a processor. For the optional implementation thatexempts one or more SLDs, if traffic and users are being monitored forexample_domain.com, for example, this SLD could be explicitly includedin a customizable exempt list to make sure that it is not blacklisted.

At 602 of the method 600, a second level domain can be identified as anintended domain. For example, the intended domain can be a domain thatthe user intended to access (e.g., a website associated with a newsnetwork). Various techniques can be utilized to identify the secondlevel domain as the intended domain as discussed herein.

Based on the identification of the second level domain as the intendeddomain, at 604, the second level domain can be added to a list of secondlevel domains that are exempt from being identified as non-intendeddomains. The list of exempt domains (as well as the whitelists and/orblacklists discussed herein) can be utilized across various users andbrowsing traffic.

FIG. 7 illustrates an example, non-limiting, method 700 for utilizing asubstring match to identify an unintended domain name in accordance withone or more embodiments described herein. The method 700 can beimplemented by a system comprising a processor.

For the optional implementation that comprises detection of a badsubstring, a way to assess whether an SLD is likely to get mostlynon-user-intended traffic is by a substring match (e.g., detect a badsubstring). A list of substrings can be built, at 702. Building the listof substrings can depend on the use case. If any of the listedsubstrings is found within a domain name, the found domain can beincluded in the blacklist. Examples of such substrings include‘adserver’, ‘-ads’, ‘ad-’, ‘ads-’, ‘analytics’, ‘cdn,’ and so forth. Byway of example and not limitation, a simple example of a substring canbe the word “advertisement” or variants thereof. Therefore, if a domainincludes the word “advertisement” or variants thereof that domain can beidentified as a non-intended domain, at 704. Accordingly, variouslistings (e.g., blacklists) can be created without the need to performevaluation beyond the domain name.

FIG. 8 illustrates an example, non-limiting, method 800 for combining adetermined blacklist with one or more external lists to create a mergedlist in accordance with one or more embodiments described herein. Themethod 800 can be implemented by a system comprising a processor.

According to another optional implementation, the blacklist can becombined (union) with one or more external lists that aim to captureaspects of what is targeted to be filtered out. Thus, at 802, asecond-level domain of the group of second-level domains can be includedin a list of identified non-intended domains. Thereafter, at 804, athird party created list of non-intended domains can be merged with thelist of identified non-intended domains.

For example, an ad server list from an internal (or external) sourcecould be targeted to be fielded out. As previously mentioned, theselists tend to be partial and relying only on them is not enough.Combining the list with one or more blacklist obtained through thevarious aspects discussed herein, can enhance the one or moreblacklists.

As discussed herein, a contradiction score can be computed for each SLDthat indicates how likely this SLD attracts mostly non-user-intendedtraffic. This score computes a measure of inconsistency betweenempirical prevalence observing in browsing history data and externalpopularity rankings.

The contradiction score can be calculated by a formula designed so thatonly domains heavily visited by non-user-intended traffic give highcontradiction scores. A cutoff is chosen and all SLDs whose scores arehigher than the cutoff can be included in the output blacklist. Thehigher the contradiction score, the more confidence that traffic to thisSLD is mostly non-user intended.

After computing the contradiction score, there are also optional stepsto perform the following. Validate the SLD to determine whether it is avalid IP address or whether HTTP or HTTPS connection can be successfullyestablished with the SLD. Detect bad substring (e.g. “advert,” “ads-”and so on) from a customizable bad substring list. Combine with externalad-server list (e.g., from an external source, from a third-partysource). Exempt some SLDs. For example, if traffic and users are beingmonitored for example_domain.com, this domain can be explicitly includedin a customizable exempt list to make sure that it is not blacklisted.

As discussed herein, user-intended and non-user intended traffic can beseparated in post-hoc browsing traces. The disclosed aspects can yield amuch more complete list of non-user-intended domains as compared toother methods. For example, the lists created can identify approximately80% to around 90% of the traffic as non-user-intended, closely in linewith conclusions from internal experiments and external studies. Otherlists can identify only about 10% to around 20% of traffic; do not listsome major ad servers, do not list domains that generate third partyrequests other than ad servers (e.g., analytics and tracking, socialmedia tools), and do not list domains that generate passive systemrequests (e.g., geo-positioning and configuration).

Further, the disclosed aspects can process existing browsing historieswithout needing user-side access. For example, a number of tools andbrowser plug-ins allow users to examine and selectively block certainthird-party requests. However, these tools need user-side installationsand actions. On the other hand, the disclosed aspects do not needuser-side access or user action at all, and enables separation ofuser-intended traffic from an existing dataset of browsing history. Inaddition to the above advantages, the disclosed aspects are easy togeneralize, and the output blacklist is easy to update, interpret, andcustomize.

The disclosed aspects are generalizable and can easily be adapted todesktop/laptop browsing traffic, and browsing traffic from countriesother than the United States. Any independent domain popularity rankingcan be used as long as its quality is reasonable. The disclosed aspectscan also be easily extended to develop a more granular output list ofdomains at lower levels than SLDs, if finer domain information isavailable.

The output list is easy to output. For example, the output blacklist canbe recomputed and updated as often as desired. Domain names andespecially names of third-party request domains do evolve over time, butnot very frequently. From various experiments conducted, updating onceevery three months could be sufficient. However, more frequent, or lessfrequent, updates can occur in accordance with the various aspectsdiscussed herein. Each update computation is very fast.

In addition, the output list is interpretable and customizable. Theoutput blacklist includes for each SLD its contraction score value, andits binary flags (if done) for invalid SLD, bad substrings, andmembership in external ad-server list. This additional informationsuggests why each SLD is on the list and the confidence level related toits relevance on the list. This allows for further customization fordifferent use cases (e.g. making the list more lenient or aggressive).It is noted that the quality of user segmentation and profiling forvarious use cases improves considerably after the non-user-intendedtraffic is filtered out using a blacklist constructed by the disclosedaspects.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate separatingintended and non-intended browsing traffic in browsing history foradvanced networks. Facilitating generic reciprocity-based channel stateinformation acquisition frameworks for advanced networks can beimplemented in connection with any type of device with a connection tothe communications network (e.g., a mobile handset, a computer, ahandheld device, etc.) any Internet of things (IoT) device (e.g.,toaster, coffee maker, blinds, music players, speakers, etc.), and/orany connected vehicles (cars, airplanes, space rockets, and/or other atleast partially automated vehicles (e.g., drones)). In some embodiments,the non-limiting term User Equipment (UE) is used. It can refer to anytype of wireless device that communicates with a radio network node in acellular or mobile communication system. Examples of UE are targetdevice, device to device (D2D) UE, machine type UE or UE capable ofmachine to machine (M2M) communication, PDA, Tablet, mobile terminals,smart phone, Laptop Embedded Equipped (LEE), laptop mounted equipment(LME), USB dongles etc. Note that the terms element, elements andantenna ports can be interchangeably used but carry the same meaning inthis disclosure. The embodiments are applicable to single carrier aswell as to Multi-Carrier (MC) or Carrier Aggregation (CA) operation ofthe UE. The term Carrier Aggregation (CA) is also called (e.g.,interchangeably called) “multi-carrier system,” “multi-cell operation,”“multi-carrier operation,” “multi-carrier” transmission and/orreception.

In some embodiments, the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves one or more UEs and/or that is coupled to other network nodes ornetwork elements or any radio node from where the one or more UEsreceive a signal. Examples of radio network nodes are Node B, BaseStation (BS), Multi-Standard Radio (MSR) node such as MSR BS, eNode B,network controller, Radio Network Controller (RNC), Base StationController (BSC), relay, donor node controlling relay, Base TransceiverStation (BTS), Access Point (AP), transmission points, transmissionnodes, RRU, RRH, nodes in Distributed Antenna System (DAS) etc.

Cloud Radio Access Networks (RAN) can enable the implementation ofconcepts such as Software-Defined Network (SDN) and Network FunctionVirtualization (NFV) in 5G networks. This disclosure can facilitate ageneric channel state information framework design for a 5G network.Certain embodiments of this disclosure can comprise an SDN controllerthat can control routing of traffic within the network and between thenetwork and traffic destinations. The SDN controller can be merged withthe 5G network architecture to enable service deliveries via openApplication Programming Interfaces (APIs) and move the network coretowards an all Internet Protocol (IP), cloud based, and software driventelecommunications network. The SDN controller can work with, or takethe place of Policy and Charging Rules Function (PCRF) network elementsso that policies such as quality of service and traffic management androuting can be synchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards canbe applied to 5G, also called New Radio (NR) access. 5G networks cancomprise the following: data rates of several tens of megabits persecond supported for tens of thousands of users; 1 gigabit per secondcan be offered simultaneously (or concurrently) to tens of workers onthe same office floor, several hundreds of thousands of simultaneous (orconcurrent) connections can be supported for massive sensor deployments;spectral efficiency can be enhanced compared to 4G; improved coverage;enhanced signaling efficiency; and reduced latency compared to LTE. Inmulticarrier system such as OFDM, each subcarrier can occupy bandwidth(e.g., subcarrier spacing). If the carriers use the same bandwidthspacing, then it can be considered a single numerology. However, if thecarriers occupy different bandwidth and/or spacing, then it can beconsidered a multiple numerology.

Referring now to FIG. 9, illustrated is an example block diagram of anexample mobile handset 900 operable to engage in a system architecturethat facilitates wireless communications according to one or moreembodiments described herein. Although a mobile handset is illustratedherein, it will be understood that other devices can be a mobile device,and that the mobile handset is merely illustrated to provide context forthe embodiments of the various embodiments described herein. Thefollowing discussion is intended to provide a brief, general descriptionof an example of a suitable environment in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset includes a processor 902 for controlling and processing allonboard operations and functions. A memory 904 interfaces to theprocessor 902 for storage of data and one or more applications 906(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 906 can be stored in the memory 904 and/or in a firmware908, and executed by the processor 902 from either or both the memory904 or/and the firmware 908. The firmware 908 can also store startupcode for execution in initializing the handset 900. A communicationscomponent 910 interfaces to the processor 902 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component910 can also include a suitable cellular transceiver 911 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 900 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 910 also facilitates communications reception from terrestrialradio networks (e.g., broadcast), digital satellite radio networks, andInternet-based radio services networks.

The handset 900 includes a display 912 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 912 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 912 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface914 is provided in communication with the processor 902 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 900, for example. Audio capabilities areprovided with an audio I/O component 916, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 916 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC(Subscriber Identity Component) in the form factor of a card SubscriberIdentity Module (SIM) or universal SIM 920, and interfacing the SIM card920 with the processor 902. However, it is to be appreciated that theSIM card 920 can be manufactured into the handset 900, and updated bydownloading data and software.

The handset 900 can process IP data traffic through the communicationscomponent 910 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 900 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 922 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 922can aid in facilitating the generation, editing, and sharing of videoquotes. The handset 900 also includes a power source 924 in the form ofbatteries and/or an AC power subsystem, which power source 924 caninterface to an external power system or charging equipment (not shown)by a power I/O component 926.

The handset 900 can also include a video component 930 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 930 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 932 facilitates geographically locating the handset 900. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 934facilitates the user initiating the quality feedback signal. The userinput component 934 can also facilitate the generation, editing andsharing of video quotes. The user input component 934 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touchscreen, for example.

Referring again to the applications 906, a hysteresis component 936facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 938 can be provided that facilitatestriggering of the hysteresis component 936 when the Wi-Fi transceiver913 detects the beacon of the access point. A SIP client 940 enables thehandset 900 to support SIP protocols and register the subscriber withthe SIP registrar server. The applications 906 can also include a client942 that provides at least the capability of discovery, play and storeof multimedia content, for example, music.

The handset 900, as indicated above related to the communicationscomponent 910, includes an indoor network radio transceiver 913 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Referring now to FIG. 10, illustrated is an example block diagram of anexample computer 1000 operable to engage in a system architecture thatfacilitates wireless communications according to one or more embodimentsdescribed herein. The computer 1000 can provide networking andcommunication capabilities between a wired or wireless communicationnetwork and a server (e.g., Microsoft server) and/or communicationdevice. In order to provide additional context for various aspectsthereof, FIG. 10 and the following discussion are intended to provide abrief, general description of a suitable computing environment in whichthe various aspects of the innovation can be implemented to facilitatethe establishment of a transaction between an entity and a third party.While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 10, implementing various aspects described hereinwith regards to the end-user device can include a computer 1000, thecomputer 1000 including a processing unit 1004, a system memory 1006 anda system bus 1008. The system bus 1008 couples system componentsincluding, but not limited to, the system memory 1006 to the processingunit 1004. The processing unit 1004 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures can also be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes read-only memory (ROM) 1027 and random access memory (RAM)1012. A basic input/output system (BIOS) is stored in a non-volatilememory 1027 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1000, such as during start-up. The RAM 1012 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1000 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), which internal hard disk drive 1014 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to aremovable diskette 1018) and an optical disk drive 1020, (e.g., readinga CD-ROM disk 1022 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1014, magnetic diskdrive 1016 and optical disk drive 1020 can be connected to the systembus 1008 by a hard disk drive interface 1024, a magnetic disk driveinterface 1026 and an optical drive interface 1028, respectively. Theinterface 1024 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1000 the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1000, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the exemplary operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1000 throughone or more wired/wireless input devices, e.g., a keyboard 1038 and apointing device, such as a mouse 1040. Other input devices (not shown)can include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touchscreen, or the like. These and other input devicesare often connected to the processing unit 1004 through an input deviceinterface 1042 that is coupled to the system bus 1008, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1044 or other type of display device is also connected to thesystem bus 1008 through an interface, such as a video adapter 1046. Inaddition to the monitor 1044, a computer 1000 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1000 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1048. The remotecomputer(s) 1048 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1050 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1052 and/or larger networks,e.g., a wide area network (WAN) 1054. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1000 isconnected to the local network 1052 through a wired and/or wirelesscommunication network interface or adapter 1056. The adapter 1056 canfacilitate wired or wireless communication to the LAN 1052, which canalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1056.

When used in a WAN networking environment, the computer 1000 can includea modem 1058, or is connected to a communications server on the WAN1054, or has other means for establishing communications over the WAN1054, such as by way of the Internet. The modem 1058, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1008 through the input device interface 1042. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1050. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, in a hotel room, or a conference room at work, withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11 (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networksoperate in the unlicensed 2.4 and 5 GHz radio bands, at an 9 Mbps(802.11a) or 54 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 16BaseT wired Ethernetnetworks used in many offices.

An aspect of 5G, which differentiates from previous 4G systems, is theuse of NR. NR architecture can be designed to support multipledeployment cases for independent configuration of resources used forRACH procedures. Since the NR can provide additional services than thoseprovided by LTE, efficiencies can be generated by leveraging the prosand cons of LTE and NR to facilitate the interplay between LTE and NR,as discussed herein.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics can be combined in any suitable manner in one or moreembodiments.

As used in this disclosure, in some embodiments, the terms “component,”“system,” “interface,” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution, and/or firmware. As anexample, a component can be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instructions, a program, and/or acomputer. By way of illustration and not limitation, both an applicationrunning on a server and the server can be a component.

One or more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components can communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by one or more processors, wherein theprocessor can be internal or external to the apparatus and can executeat least a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confer(s) at least in part the functionalityof the electronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or.” That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “mobile device equipment,” “mobile station,”“mobile,” subscriber station,” “access terminal,” “terminal,” “handset,”“communication device,” “mobile device” (and/or terms representingsimilar terminology) can refer to a wireless device utilized by asubscriber or mobile device of a wireless communication service toreceive or convey data, control, voice, video, sound, gaming orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably herein and with reference to the relateddrawings. Likewise, the terms “access point (AP),” “Base Station (BS),”BS transceiver, BS device, cell site, cell site device, “Node B (NB),”“evolved Node B (eNode B),” “home Node B (HNB)” and the like, areutilized interchangeably in the application, and refer to a wirelessnetwork component or appliance that transmits and/or receives data,control, voice, video, sound, gaming or substantially any data-stream orsignaling-stream from one or more subscriber stations. Data andsignaling streams can be packetized or frame-based flows.

Furthermore, the terms “device,” “communication device,” “mobiledevice,” “subscriber,” “customer entity,” “consumer,” “customer entity,”“entity” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based on complex mathematical formalisms), which canprovide simulated vision, sound recognition and so forth.

Embodiments described herein can be exploited in substantially anywireless communication technology, comprising, but not limited to,wireless fidelity (Wi-Fi), global system for mobile communications(GSM), universal mobile telecommunications system (UMTS), worldwideinteroperability for microwave access (WiMAX), enhanced general packetradio service (enhanced GPRS), third generation partnership project(3GPP) long term evolution (LTE), third generation partnership project 2(3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA),Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacytelecommunication technologies.

The various aspects described herein can relate to New Radio (NR), whichcan be deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE), for example. It should benoted that although various aspects and embodiments have been describedherein in the context of 5G, Universal Mobile Telecommunications System(UMTS), and/or Long Term Evolution (LTE), or other next generationnetworks, the disclosed aspects are not limited to 5G, a UMTSimplementation, and/or an LTE implementation as the techniques can alsobe applied in 3G, 4G, or LTE systems. For example, aspects or featuresof the disclosed embodiments can be exploited in substantially anywireless communication technology. Such wireless communicationtechnologies can include UMTS, Code Division Multiple Access (CDMA),Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), GeneralPacket Radio Service (GPRS), Enhanced GPRS, Third Generation PartnershipProject (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2)Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), EvolvedHigh Speed Packet Access (HSPA+), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or anotherIEEE 802.XX technology. Additionally, substantially all aspectsdisclosed herein can be exploited in legacy telecommunicationtechnologies.

As used herein, “5G” can also be referred to as NR access. Accordingly,systems, methods, and/or machine-readable storage media for facilitatinglink adaptation of downlink control channel for 5G systems are desired.As used herein, one or more aspects of a 5G network can comprise, but isnot limited to, data rates of several tens of megabits per second (Mbps)supported for tens of thousands of users; at least one gigabit persecond (Gbps) to be offered simultaneously to tens of users (e.g., tensof workers on the same office floor); several hundreds of thousands ofsimultaneous connections supported for massive sensor deployments;spectral efficiency significantly enhanced compared to 4G; improvementin coverage relative to 4G; signaling efficiency enhanced compared to4G; and/or latency significantly reduced compared to LTE.

Systems, methods and/or machine-readable storage media for facilitatinga two-stage downlink control channel for 5G systems are provided herein.Legacy wireless systems such as LTE, Long-Term Evolution Advanced(LTE-A), High Speed Packet Access (HSPA) etc. use fixed modulationformat for downlink control channels. Fixed modulation format impliesthat the downlink control channel format is always encoded with a singletype of modulation (e.g., quadrature phase shift keying (QPSK)) and hasa fixed code rate. Moreover, the forward error correction (FEC) encoderuses a single, fixed mother code rate of ⅓ with rate matching. Thisdesign does not take into the account channel statistics. For example,if the channel from the BS device to the mobile device is very good, thecontrol channel cannot use this information to adjust the modulation,code rate, thereby unnecessarily allocating power on the controlchannel. Similarly, if the channel from the BS to the mobile device ispoor, then there is a probability that the mobile device might not ableto decode the information received with only the fixed modulation andcode rate. As used herein, the term “infer” or “inference” refersgenerally to the process of reasoning about, or inferring states of, thesystem, environment, user, and/or intent from a set of observations ascaptured via events and/or data. Captured data and events can includeuser data, device data, environment data, data from sensors, sensordata, application data, implicit data, explicit data, etc. Inference canbe employed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationprocedures and/or systems (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, anddata fusion engines) can be employed in connection with performingautomatic and/or inferred action in connection with the disclosedsubject matter.

In addition, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, machine-readable media,computer-readable (or machine-readable) storage/communication media. Forexample, computer-readable media can comprise, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media. Of course, thoseskilled in the art will recognize many modifications can be made to thisconfiguration without departing from the scope or spirit of the variousembodiments

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: determiningrespective contradiction values for second-level domains of a group ofsecond-level domains in observed browsing history traffic; andseparating the observed browsing history traffic into intended networkbrowsing traffic and non-intended network browsing traffic based on therespective contradiction values, wherein the intended network browsingtraffic comprises information related to user-initiated browsingbehavior, and wherein the non-intended network browsing trafficcomprises background activity void of the information related to theuser-initiated browsing behavior, and wherein the respectivecontradiction values indicate levels of inconsistency between respectivepopularities of the second-level domains and associated subdomains inthe observed browsing history traffic and determined respectivepopularity rankings of the second-level domains.
 2. The system of claim1, wherein the separating comprises identifying a second-level domain ofthe group of second-level domains as the non-intended network browsingtraffic based on a contradiction value for the second-level domaindetermined to be above a threshold level.
 3. The system of claim 2,wherein the operations further comprise: adding the second-level domainto a list of identified non-intended domains, and utilizing the list tofacilitate the separating the intended network browsing traffic from thenon-intended network browsing traffic.
 4. The system of claim 1, whereinthe determining comprises evaluating the respective popularities of thesecond-level domains and associated subdomains in the observed browsinghistory data and respective third-party popularity rankings of thesecond-level domains.
 5. The system of claim 1, wherein the operationsfurther comprise: initiating a network connection with a second leveldomain selected from the group of second-level domains; and determiningthe second level domain is a valid second level domain based on asuccessful network connection with the second level domain.
 6. Thesystem of claim 1, wherein the operations further comprise: initiating anetwork connection with a second level domain selected from the group ofsecond-level domains; determining the second level domain is an invalidsecond level domain based on an unsuccessful network connection with thesecond level domain; and adding the second level domain to a list ofidentified non-intended domains, wherein the list is utilized tofacilitate the separating the intended network traffic from thenon-intended network browsing traffic.
 7. The system of claim 1, whereinthe operations further comprise: identifying a second level domain as anintended domain; and adding the second level domain to a list of secondlevel domains that are exempted from being identified as non-intendeddomains.
 8. The system of claim 1, wherein the operations furthercomprise: generating a list of substrings; and identifying a secondlevel domain as a non-intended domain based on a determination that thesecond level domain comprises a substring included in the list ofsubstrings.
 9. The system of claim 1, wherein the operations furthercomprise: including a second-level domain of the group of second-leveldomains to a list of identified non-intended domains; and merging athird party created list of non-intended domains with the list ofidentified non-intended domains.
 10. The system of claim 1, wherein theobserved browsing history traffic is a sample of available browsinghistory traffic, and wherein the operations further comprise: includingthe second-level domains associated with the non-intended networkbrowsing traffic in a first data store and the second-level domainsassociated with the intended network browsing traffic in a second datastore; applying the first data store and the second data store to otheravailable browsing history traffic not including the sample of availablebrowsing history traffic; and separating the other available browsinghistory traffic into the intended network browsing traffic and thenon-intended network browsing traffic.
 11. A method, comprising:evaluating, by a system comprising a processor, respective contradictionscores for second-level domains of a group of second-level domains ofobserved browsing history traffic; and identifying, by the system,intended network browsing traffic and non-intended network browsingtraffic of the observed browsing history traffic based on the respectivecontradiction scores, wherein the respective contradiction scoresindicate a level of inconsistency between the respective popularities ofsecond-level domains of the group of second-level domains and associatedsubdomains in observed browsing history traffic and respective externalpopularity rankings of the second-level domains, and wherein theidentifying comprises determining that the intended network browsingtraffic is generated based on a specified request and that thenon-intended network browsing traffic is generated based on a passiverequest.
 12. The method of claim 11, wherein the observed browsinghistory traffic is first observed browsing history traffic, and whereinthe method further comprises: generating, by the system, a first listthat comprises second level domains associated with the intended networkbrowsing traffic and a second list that comprises the second leveldomains associated with the non-intended network browsing traffic; andseparating, by the system, the second level domains of second observedbrowsing history traffic based on an application of the first list andthe second list to the second observed browsing history traffic.
 13. Themethod of claim 12, further comprising: identifying, by the system, afirst second-level domain of the group of second-level domains as theintended network browsing traffic based on a first contradiction scoreof the respective contradiction scores not satisfying a threshold value;and identifying, by the system, a second second-level domain of thegroup of second-level domains as the non-intended network browsingtraffic based on a second contradiction score of the respectivecontradiction scores satisfying the threshold value.
 14. The method ofclaim 11, wherein the evaluating comprises evaluating respectivepopularities of the second-level domains and associated subdomains inthe observed browsing history data and the respective externalpopularity rankings of the second-level domains.
 15. The method of claim11, further comprising: generating, by the system, a list of substrings;and identifying, by the system, a second level domain as a non-intendeddomain based on a determination that the second level domain comprises asubstring included in the list of substrings.
 16. The method of claim11, further comprising: including, by the system, a second-level domainof the group of second-level domains to a list of identifiednon-intended domains; and merging, by the system, an external list ofnon-intended domains to the list of identified non-intended domains. 17.The method of claim 11, further comprising: initiating, by the system, anetwork connection with a second level domain selected from the group ofsecond-level domains; determining, by the system, the second leveldomain is a valid second level domain based on a successful networkconnection with the second level domain or is an invalid second leveldomain based on an unsuccessful network connection with the second leveldomain; and adding, by the system, the invalid second level domain to alist of identified non-intended domains, wherein the list is utilized tofacilitate the separating the intended network traffic from thenon-intended network browsing traffic.
 18. The method of claim 11,further comprising: identifying, by the system, a second level domain asan intended domain; and adding, by the system, the second level domainto a list of second level domains that are exempted from beingidentified as non-intended domains.
 19. A non-transitorymachine-readable medium, comprising executable instructions that, whenexecuted by a processor, facilitate performance of operations,comprising: determining, for a first user equipment selected from agroup of user equipment associated with a network, respectivecontradiction values for first domains of first browsing historytraffic; identifying the first domains as a first group of domains and asecond group of domains, wherein the first group of domains comprisesdomains associated with intentional network traffic, and wherein thesecond group of domains comprises domains associated with unintentionalnetwork traffic, wherein the intentional network traffic is activelyinitiated based on user input to the first user equipment, and whereinthe unintentional network traffic comprises non-user requests initiatedby an operating system of the first user equipment; and applying thefirst group of domains and the second group of domains to secondbrowsing history traffic associated with a second user equipmentselected from the group of user equipment, wherein the respectivecontradiction scores indicate a level of inconsistency betweenrespective popularities of the first domains and associated subdomainsin the first browsing history traffic and respective external popularityrankings of the first domains.
 20. The non-transitory machine-readablemedium of claim 19, wherein the operations further comprise: generatinga whitelist that comprises the first group of domains and a blacklistthat comprises the second group of domains; and applying the whitelistand the blacklist to user equipment in the group of user equipment,comprising the second user equipment, wherein the group of userequipment is configured to operate according to a fifth generationcommunications network protocol.